In this paper, we introduce $\textbf{GS-SLAM}$ that first utilizes 3D
Gaussian representation in the Simultaneous Localization and Mapping (SLAM)
system. It facilitates a better balance between efficiency and accuracy.
Compared to recent SLAM methods employing neural implicit representations, our
method utilizes a real-time differentiable splatting rendering pipeline that
offers significant speedup to map optimization and RGB-D re-rendering.
Specifically, we propose an adaptive expansion strategy that adds new or
deletes noisy 3D Gaussian in order to efficiently reconstruct new observed
scene geometry and improve the mapping of previously observed areas. This
strategy is essential to extend 3D Gaussian representation to reconstruct the
whole scene rather than synthesize a static object in existing methods.
Moreover, in the pose tracking process, an effective coarse-to-fine technique
is designed to select reliable 3D Gaussian representations to optimize camera
pose, resulting in runtime reduction and robust estimation. Our method achieves
competitive performance compared with existing state-of-the-art real-time
methods on the Replica, TUM-RGBD datasets. The source code will be released
upon acceptance.

本文介绍了一种名为 GS-SLAM 的算法，它在同时定位与地图构建（SLAM）系统中首次使用了 3D 高斯表示方法，实现了效率和准确性之间的更好平衡。与使用神经隐式表示的最新 SLAM 方法相比，我们的方法采用了实时可微分雀斑光照渲染流水线，大大加速了地图优化和 RGB-D 重渲染。具体而言，我们提出了一种自适应扩张策略，通过添加新的或删除噪音 3D 高斯来有效重构新观测到的场景几何并改善先前观测区域的建图。这种策略对于将 3D 高斯表示扩展到重建整个场景而不是在现有方法中合成静态物体至关重要。此外，在位姿跟踪过程中，设计了一种有效的从粗到细的技术，以选择可靠的 3D 高斯表示来优化相机姿态，从而减少运行时间并实现强健的估计。我们的方法在 Replica 和 TUM-RGBD 数据集上与现有的最新实时方法具有竞争力的性能。源代码将在获批后发布。